abstract = "Genetic Improvement (GI) performs a search at the
level of source code to find the best variant of a
baseline system that improves non-functional properties
while maintaining functionality, with noticeable
results in several domains. There a many aspects of
this general approach that are currently being
explored. In particular, this work deals to the way in
which the search is guided to efficiently explore the
search space of possible software versions in which GI
operates. The proposal is to integrate Novelty Search
(NS) within the GISMOE GI framework to improve
KinectFusion, which is a vision-based Simultaneous
Localization and Mapping (SLAM) system that is used for
augmented reality, autonomous vehicle navigation, and
many other real-world applications. This is one of a
small set of works that have successfully combined NS
with a GP system, and the first time that it has been
used for software improvement. To achieve this, we
propose a new behaviour descriptor for SLAM algorithms,
based on state-of-the-art benchmarking and present
results that show that NS can produce significant
improvement gains in a GI setting, when considering
execution time and trajectory estimation as the main
performance criteria.",